1. Volume 104, Issue 2, Pages 488-495 (January 2013)
Inherent Relationships among Different Biophysical Prediction Methods for Intrinsically Disordered Proteins 
Fan Jin, Zhirong Liu 
Biophysical Journal 
Volume 104, Issue 2, Pages (January 2013)
DOI: /j.bpj
Copyright © 2013 Biophysical Society Terms and Conditions

2. Figure 1
Average radius of gyration (〈Rg〉) of HPQ chains with sequence length N = 150 as a function of the hydrophobic-residue fraction (〈H〉) when the charged-residue fraction is: (from bottom to top) 〈Q〉 = 0.0, 0.1, 0.2, …, 0.9. (Points) Simulation results averaged over five random sequences. (Lines) Fit of the simulation results as given by Eqs. 1 and 2.
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions

3. Figure 2
Performance of the CH-plot and packing-density algorithms on the HPQ model. The ordered and disordered datasets are shown (circles and rectangles), respectively. (a) Performance of the CH-plot with the determined boundary shown (solid line). (b) Performance of the packing-density algorithm: (open symbols) the successful predictions; (solid symbols) the false predictions. The optimized window size was 55 and the critical packing-density value was 52.8.
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions

4. Figure 3
Relationships between the CH-plot and packing-density algorithms in the HPQ model. (a) The CH-plot boundary (red) and the packing-density contour (blue) in the (〈H〉, 〈Q〉) plane. (Thick line) Contour line with the critical packing-density value. (Arrows) Normal-lines normal to the boundaries in the CH-plot and packing-density algorithms. (b and c) Correlations between the packing density and the CH-plot projection (b) for three residues and (c) at the polypeptide level.
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions

5. Figure 4
Correlations between the CH-plot and packing-density algorithms in real systems. (a) Correlation at the 20-residue level. (b) Correlation at the protein level in the SCOP (blue circles) and DisProt (red rectangles) datasets. (Straight lines) Linear fits of data; the correlation coefficients are also shown. To reduce the overwhelming number of SCOP data points, the same numbers of SCOP and DisProt data points were used in the global linear fit.
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions

6. Figure 5
Relationships between the pairwise-energy algorithm and other algorithms. (a and b) Correlation between the pairwise-energy and packing-density algorithms. (c and d) Correlation between the pairwise-energy and CH-plot algorithms. The first principle component of the pairwise-energy matrix was used in the analysis at the residue level (a and c). SCOP and DisProt datasets (blue circles and red rectangles), respectively (b and d).
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions

7. Figure 6
Performance of various physico-chemical properties in order/disorder prediction. Scales for five properties are shown at the bottom of the figure. The properties (denoted as x) were also normalized into a comparable scale (x∗) using the mean value (〈x〉) and the distribution width (σx) of the positive (disordered) set: x∗ = (x – 〈x〉)/σx, which was adopted in aligning different cures.
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions

8. Figure 7
Correlations between the amyloid propensity and various physico-chemical properties used in order/disorder prediction.
Biophysical Journal  , DOI: ( /j.bpj )
Copyright © 2013 Biophysical Society Terms and Conditions